Body Worn EMG Controlling of Assistive Robotic Arm for Stroke Rehabilitation

描述(申请人提供)：中风是美国成年人致残的首要原因和第三大死亡原因。世界上每年约有1500万人中风，美国每年有超过70万人中风。大脑半球中风后，身体一侧肢体的运动控制通常会受到影响。许多患者在身体的对侧遭受各种致残的身体症状。特别是，上肢(手臂、手、手指/拇指)的灵巧性经常受到影响，限制了日常生活(ADL)的基本活动，如吃饭、穿衣、写作或打字。一些机电设备已经被设计成机器人辅助中风康复的辅助工具，以改善上肢功能。大多数设备可以帮助使用者以被动的方式(当他们放松时)或以主动的方式(当他们打算为运动做出贡献时)进行涉及到他们瘫痪的肢体的重复运动的练习。表面肌电信号包含丰富的运动控制信息，从中可以检测出使用者的意图。然而，由于上肢的灵巧性，大多数功能任务通常是通过多肌肉复杂的时间和空间协调来完成的。通过肌肉和自由度之间的一对一映射来实现对这样多个自由度的控制是不可行的。模式识别技术近年来在肌电控制系统的发展中引起了越来越多的关注。最近，我们提出了一种新的框架，使用高密度表面肌电记录和模式识别分析来检测中风幸存者。我们的研究表明，对涉及患肢的多达20个手臂、手、手指/拇指动作的分类可以获得高精度，这表明利用肌电模式识别技术可以从中风受试者的瘫痪肌肉中提取大量的运动控制信息。这些信息将潜在地实现对辅助装置的意志控制，从而促进受影响肢体的功能恢复。在第一阶段，我们将演示用于机器人控制的高密度肌电系统的可行性，以允许中风患者自愿控制辅助工具。在第二阶段，肌电控制系统将与辅助机器人完全集成，以实施改进的中风康复，并在患者群体上进行测试。

DESCRIPTION (provided by applicant): Stroke is the leading cause of adult disability and the third leading cause of death in the United States. Approximately 15 million people in the world, and more than 700,000 people in the United States, experience a stroke each year. Following a hemispheric stroke, motor control of extremities on one side of the body is usually affected. Many patients suffer a variety of disabling physical symptoms on the contralesional side of the body. In particular, upper limb (arm, hand, finger/thumb) dexterity is often affected, limiting fundamental activities of daily living (ADL) such as eating, dressing, writing or typing. A number of mechatronic devices have been designed as assistive tools for robot-aided stroke rehabilitation to improve upper limb function. Most of the devices can assist users to perform exercises which involve repetitive movement of their paretic limb in a passive way (as they relax), or in an active way (as they intend to contribute to the movement). Surface electromyogram (EMG) signals contain rich motor control information, from which the user's intention can be detected. Due to the upper-limb dexterity, however, most functional tasks are generally accomplished through complex temporal and spatial coordination of multiple muscles. It is unfeasible to realize the control of such multiple DOFs via one-to-one mapping (between a muscle and a DOF). Pattern recognition techniques have recently attracted increasing attention in the development of myoelectric control systems. Recently, we have presented a novel framework for stroke survivors using high density surface EMG recording and pattern recognition analysis. Our research demonstrates that high accuracies can be obtained in classification of up to 20 arm, hand, finger/thumb movements involving the affected limb, suggesting that with myoelectric pattern recognition techniques substantial motor control information can be extracted from the paretic muscles of stroke subjects. Such information will potentially enable volitional control of assistive devices, thereby facilitating the functional restoration for the affected limb. In phase I, we will demonstrate the feasibility of a high densit EMG system for robot control to allow stroke subjects volitional control of assistive tools. In phase II, the myoelectric control system will be fully integrated with the assistive robot for implementing improved stroke rehabilitation and tested on a patient population.